Machine learning framework for scour detection across multiple offshore wind farms

This paper proposes a global scour detection framework for monopile foundations across offshore wind farms (OWFs) using vibration data acquired from a single accelerometer mounted on the turbine tower. The proposed framework is designed to address the real-world complexity and heterogeneity of offshore wind systems operating under diverse environmental, geotechnical, and structural conditions across multiple sites. To validate the framework, numerically generated acceleration data are obtained for various offshore wind turbines (OWTs) distributed across multiple OWFs using an integrated OpenFAST-bespoke soil–structure interaction (SSI) model. The generated dataset accounts for a wide array of operating conditions, including varying sea states, soil properties, turbine structural characteristics, and site-specific scour scenarios. For each OWT, acceleration responses are simulated using foundation stiffness values derived from the SSI model, reflecting the dynamic behaviour variations corresponding to site conditions, turbine characteristics and scour state. A multi-source-domain generalization (DG) strategy is then employed, in which a model is trained using a combined dataset containing one representative turbine per farm (referred to as source turbines) and subsequently evaluated on the remaining previously unseen turbines (referred to as target turbines) across geographically disparate wind farms. The results demonstrate that the proposed framework accurately identifies scour states across multiple target turbines without target-domain data during training, outperforming single-source and non-DG approaches. Model performance is strongly influenced by source-turbine selection, while increasing the number of source turbines improves prediction accuracy, particularly for early-stage scour. Furthermore, vibration measurements collected near the tower base substantially improve scour detection performance. The DG methods maintain high accuracy for intermediate and deep scour depths but struggle to detect shallow scour of 1 m.